The forensic study of mobile apps that use application-level encryption requires the decryption of the data they generate. Such a decryption requires the knowledge of the encryption algorithm and key. Determining them requires, however, a quite complex analysis that is time-consuming, error prone, and often beyond the reach of many forensic examiners. In this paper, we tackle this problem by devising a framework able to automate the decryption of these data when third-party encryption libraries or platforms are used. Our framework is based on the use of dynamic instrumentation of app’s binary code by means of hooking, which enables it to export the plaintext of data after they have been decrypted by the app, as well as the corresponding encryption key and parameters. This framework has been conceived to be used only with test devices used for forensic study purposes, and not with devices that need to be forensically analyzed. We describe the architecture of the framework as well as the implementation of its components and of the hooks supporting three prominent and popular encryption libraries, namely SQLCipher, Realm and Jetpack Security. Also, we validate our framework by comparing its decryption results against those published in the literature for Wickr Me, Signal, Threema, and Element.

We propose a framework for structuring the description and results of the forensic analysis of actions of investigative interest in digital applications, and for automated reasoning on such actions. A high level of abstraction is suitable for forensic stakeholders that are not ICT experts; other levels are suitable for automating experiments on the devices to establish traces left by actions, and for associating the results of the experiments. Such results are used in a computational logic framework to conclude evidence on the occurrence of actions. The evidence can be presented to stakeholders or used in further automated reasoning, and traced back to data on the device.

Interoperability between different cloud platforms is a critical requirement for letting users to smoothly switch between different cloud providers and combine their services. However, the lack of standard interfaces to access these cloud platforms may result in the vendor lock-in situation, whereby users are locked into a specific cloud provider. In this paper, we present EasyCloud, a toolkit able to effectively support the creation and usage of Multi-cloud Systems (MSs) by providing interoperability, platform independence, effective resource provisioning, and ease of use. We describe its architecture and implementation, and experimentally assess the performance of EasyCloud, and compare it to existing alternative MS toolkits that are representative of the state-of-the-art. Our results clearly show that EasyCloud is highly scalable, quite efficient, and outperforms the other alternative toolkits.

Summary We consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co‐location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time‐varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game‐theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

Summary A typical problem that arises when devising a novel resource management strategy for virtualized infrastructures is how to experimentally assess its ability of achieving its design goals and whether it advances the state of the art. Among the available options, the use of a physical testbed is usually considered to be the most appropriate because of its high degree of accuracy. Unfortunately, however, physical testbeds are characterized by a limited controllability of the experimental conditions. Moreover, their implementation is usually a complex and time‐consuming task that requires the integration of many software components that need to interact among them in non‐trivial ways. In this paper, we address the aforementioned issues by proposing Prometheus, a toolkit specifically designed to support the configuration, deployment, and use of physical testbeds suitable to perform experimental studies of resource management strategies and that provides a high degree of controllability and low implementation costs. We discuss the design, implementation, and use of Prometheus, and we show how it can be used in practice to configure and deploy a physical testbed by providing various examples of experimental activities that can be carried out by means of it.

Summary Cloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

Summary Modern cloud data centers rely on server consolidation (the allocation of several virtual machines on the same physical host) to minimize their costs. Choosing the right consolidation level (how many and which virtual machines are assigned to a physical server) is a challenging problem, because contemporary multitier cloud applications must meet service level agreements in face of highly dynamic, nonstationary, and bursty workloads. In this paper, we deal with the problem of achieving the best consolidation level that can be attained without violating application service level agreements. We tackle this problem by devising fuzzy controller for consolidation and QoS (FC2Q), a resource management framework exploiting feedback fuzzy logic control, that is able to dynamically adapt the physical CPU capacity allocated to the tiers of an application in order to precisely match the needs induced by the intensity of its current workload. We implement FC2Q on a real testbed and use this implementation to demonstrate its ability of meeting the aforementioned goals by means of a thorough experimental evaluation, carried out with real‐world cloud applications and workloads. Furthermore, we compare the performance achieved by FC2Q against those attained by existing state‐of‐the‐art alternative solutions, and we show that FC2Q works better than them in all the considered experimental scenarios. Copyright © 2014 John Wiley & Sons, Ltd.

Cloud computing is growing in popularity among computing paradigms for its appealing property of considering "Everything as a Service". The goal of a Cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the Cloud infrastructure, for running Cloud services, plays a primary role. Unfortunately, the minimization of QoS violations and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this paper, we propose a framework to automatically manage computing resources of Cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.

ShareGrid is a peer-to-peer desktop grid aimed at satisfying the computing needs of the small research laboratories located in the Piedmont area in Northern Italy. Share- Grid adopts a cooperative approach, in which each participant allows the other ones to use his/her own resources on a reciprocity basis. ShareGrid is based on the OurGrid middleware, that provides a set of mechanisms enabling participating entities to quickly, fairly, and securely share their resources. In this paper we report our experience in designing, deploying, and using ShareGrid, and we describe the applications using it, as well as the lessons we learned, the problems that still remain open, and some possible solutions to them.

Desktop grids are being increasingly used as the execution platform for a variety of applications that can be structured as bag-of-tasks (BoT). Scheduling BoT applications on desktop grids has thus attracted the attention of the scientific community, and various schedulers tailored towards them have been proposed in the literature. However, previous work has focused on scheduling a single BoT application at a time, thus neglecting other scenarios in which several users submit multiple BoT applications at the same time. This paper aims at filling this gap by proposing a set of scheduling algorithms able to deal with multiple BoT applications. The performance of these algorithm has been evaluated, by means of simulation, for a large set of operational scenarios obtained by varying both the workload submitted to the desktop grid and the characteristics of the involved resources. Our results show that, although there is no a clear winner among the proposed solutions, knowledge-free strategies (that is, strategies that do not require any information concerning the applications or the resources) can provide good performance.

Abstract The exploration in many scientific disciplines (e.g. High‐Energy Physics, Climate Modeling, and Life Sciences) involves the production and the analysis of massive data collections, whose archival, retrieval, and analysis require the coordinated usage of high‐capacity computing, network, and storage resources. To obtain satisfactory performance, these applications require the availability of a high‐performance, reliable data transfer mechanisms, able to minimize the data transfer time that often dominates their execution time. In this paper we present the File Mover, an efficient data transfer system specifically tailored to the needs of data‐intensive applications, that exploits the overlay networks paradigm to provide superior performance with respect to conventional file transfer systems. An extensive experimental evaluation, carried out by means of a proof‐of‐concept implementation of the File Mover for a variety of network scenarios, shows the ability of the File Mover to outperform alternative data transfer systems. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Data‐intensive Grid applications require the availability of tools able to transfer very large files in the shortest amount of time. Many file‐transfer tools, based on solutions aimed at overcoming the limitations imposed by the TCP protocol, have recently been developed. In this paper we experimentally compare the performance of some of these tools in various network scenarios by running experiments on PlanetLab, an open platform for the development, deployment, and access of planetary‐scale services, that comprises hundreds of hosts scattered across the globe. Our results show that solutions based on UDP and adopting rate‐based algorithms result in better performance than other alternatives in most cases, while solutions based on TCP achieve similar performance only under specific circumstances. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper we present the File Mover, a data transfer system designed to optimize the transfer of potentially very large files. The File Mover relies on an overlay network architecture, where a set of machines cooperate in the transfer by forwarding among them portions of the files being transferred. Data transfer times are minimized by choosing, for each transfer the set of relays that maximize the expected throughput. Preliminary experiments show that the File Mover is able to profitably exploit existing network paths not chosen by IP routing algorithms, thereby enhancing file transfer performance.